A Comparison Study between Integrated OBFARX-NN and OBF-NN for Modeling of Nonlinear Systems in Extended Regions of Operation

Haslinda Zabiri; M. Ariff; Lemma Dendena Tufa; Marappagounder Ramasamy

doi:10.4028/www.scientific.net/AMM.625.382

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A Comparison Study between Integrated OBFARX-NN and OBF-NN for Modeling of Nonlinear Systems in Extended Regions of Operation

Abstract:

In this paper the combination of linear and nonlinear models in parallel for nonlinear system identification is investigated. A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters-Auto regressive with exogenous input (OBFARX) and a nonlinear neural network (NN) models is developed. The model performance is then compared against previously developed parallel OBF-NN model in a nonlinear CSTR case study in extended regions of operation (i.e. extrapolation capability).

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Periodical:

Applied Mechanics and Materials (Volume 625)

Pages:

382-385

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.625.382

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Online since:

September 2014

Authors:

Haslinda Zabiri*, M. Ariff, Lemma Dendena Tufa, Marappagounder Ramasamy

Keywords:

Modeling, NN, Nonlinear System, OBFARX, Parallel Integration

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References

[1] L. Ljung, Perspectives on System Identification, Proceedings of 17th IFAC World Congress, pp.7172-7184, July, (2008).

Google Scholar

[2] G. Gregorc ic, and G. Lightbody, Nonlinear system identification: From multiple-model networks to Gaussian processes, Engineering Applications of Artificial Intelligence 21 (2008) 1035–1055.

DOI: 10.1016/j.engappai.2007.11.004

Google Scholar

[3] M. Lawrynczuk, Computationally efficient nonlinear model predictive control based on neural Wiener models, Neurocomputing (2010), doi: 10. 1016/j. neucom. 2010. 03. 014.

DOI: 10.1016/j.neucom.2010.03.014

Google Scholar

[4] Nelles, O., 2001, Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin.

Google Scholar

[5] H Zabiri, M. Ramasamy, T D Lemma, and A Maulud, Journal of Process Control, 23 (10), 1562-1566.

Google Scholar

[6] M. Madakyaru, A. Narang, S. C. Patwardhan, Industrial & Engineering Chemistry Research, 48(19), 8966-8979.

Google Scholar

[7] Lemma Dendena Tufa 2007, Control Relevant System Identification Using Orthonormal Basis Filter Models, Ph.D. Thesis, Universiti Teknologi Petronas.

Google Scholar